基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较
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西安科技大学测绘科学与技术学院

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S157.1

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目);国家重点研发计划政府间国际科技创新合作重点专项项目;水利部重大科技项目;陕西省自然科学基金;陕西省教育厅基金


Comparison of erosion monitoring methods in the Pisha sandstone areas of the Chinese Loess Plateau based on UAV-SfM data
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College of Geomatics,Xi''''an University of Science and Technology,Xi’an

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan); The National Key Research and Development Program(Key Special Project of International Science and Technology Innovation Cooperation among Governments); Major Science and Technology Projects of the Ministry of Water Resources; Shaanxi Natural Science Foundation; Shaanxi Education Department Fund

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    摘要:

    复杂地形及沟谷陡坡的土壤侵蚀监测一直是土壤侵蚀监测的难点。基于无人机的动态恢复结构(Unmanned Aerial Vehicle – Structure from Motion,UAV-SfM)的三维点云重建技术可高效的获取大范围地形数据,并以其成本低廉、简单便携等优点已成为地表变化过程监测重要数据来源之一。针对UAV-SfM地形变化监测算法缺乏深入的比较研究,限制了其在土壤侵蚀、泥沙输移过程研究中的应用。为比较地形变化监测算法在黄土高原砒砂岩区的适用性,以皇甫川流域特拉沟一支沟为研究对象,采用无人机摄影测量技术获取2022年7月至2023年3月影像,结合SfM生成三维点云数据,比较分析了(Digital Elevation Model of Difference (DoD)、Cloud to Cloud(C2C)、Cloud to Mesh(C2M)、Multiscale Model to Model Cloud Comparison(M3C2))等四种算法的侵蚀产沙监测精度,并分析了点云密度变化对各方法精度的影响。研究表明:(1)四种常用算法在空间上都能监测到大幅度地表变化。四种方法中,以M3C2算法的结果最优,线性拟合结果最好(R2=0.953,p<0.01)且综合误差最小(MAE=0.0161m,MRE=3.37%,RMSE=0.0194m),C2M算法其次,DoD算法再次,而C2C算法结果最差。(2)通过比较,DoD算法仅适用于平坦区域的快速检测,坡度陡峭的区域监测侵蚀沉积量存在高估的现象。(3)M3C2和C2C算法对点云密度变化敏感,而C2M和DoD受点云密度变化影响较小。本研究结果可为黄土高原砒砂岩地区基于UAV-SfM的侵蚀产沙监测方法的选择提供参考。

    Abstract:

    Detection of soil erosion in complex terrain and steep slopes has always been a challenge. The 3D point clouds achieved by the Unmanned Aerial Vehicle-Structure from Motion (UAV-SfM) technology provides an efficient and cost-effective method for obtaining large-scale terrain data, making it an important data source for monitoring land surface changes. However, there is a lack of comprehensive research on UAV-SfM terrain change monitoring algorithms, limiting its application in the study of soil erosion and sediment transport processes. This study assessed the accuracy of four commonly used geomorphic change detection algorithms in the Pisha sandstone area of the Loess Plateau, including Digital Elevation Model of Difference (DoD), Cloud to Cloud (C2C), Cloud to Mesh (C2M), and Multiscale Model to Model Cloud Comparison (M3C2). . Point cloud data employed to operate the four algorithms were produced using the SfM technique based on images acquired by UAV between July 2022 and March 2023. The impact of point density changes in the accuracy of the employed algorithms was also investigated. Results showed that all four algorithms were capable of effectively monitoring large surface changes. Among them, the M3C2 algorithm performed the best with the highest accuracy (R2 = 0.953, p <0.01) and the lowest error (MAE = 0.0161m, MRE = 3.37%, RMSE = 0.0194m), followed by the C2M algorithm. The DoD algorithm was only suitable for flat areas and yielded overestimated results for steep sloping areas. The M3C2 and C2C algorithms were sensitive to point cloud density, while the C2M and DoD algorithms were lesssensitive. The study provided a useful reference for the selection of erosion monitoring methods for the Pisha sandstone areas.

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  • 收稿日期:2023-12-01
  • 最后修改日期:2024-01-03
  • 录用日期:2024-01-04
  • 在线发布日期: 2024-04-29
  • 出版日期: